A combined model reduction algorithm for controlled biochemical systems.

ABSTRACT:

Background

Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest.

Methods

In this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation

SUBMITTER: Snowden TJ 

PROVIDER: S-EPMC5307760 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature